Search

New genome assemblies have been arriving at a rapidly increasing pace, thanks to decreases in sequencing costs and improvements in third-generation sequencing technologies1–3. For example, the number of vertebrate genome assemblies currently in the NCBI (National Center for Biotechnology Information) database4 increased by more than 50% to 1,485 assemblies in the year from July 2018 to July 2019. In addition to this influx of assemblies from different species, new human de novo assemblies5 are being produced, which enable the analysis of not only small polymorphisms, but also complex, large-scale structural differences between human individuals and haplotypes. This coming era and its unprecedented amount of data offer the opportunity to uncover many insights into genome evolution but also present challenges in how to adapt current analysis methods to meet the increased scale. Cactus6, a reference-free multiple genome alignment program, has been shown to be highly accurate, but the existing implementation scales poorly with increasing numbers of genomes, and struggles in regions of highly duplicated sequences. Here we describe progressive extensions to Cactus to create Progressive Cactus, which enables the reference-free alignment of tens to thousands of large vertebrate genomes while maintaining high alignment quality. We describe results from an alignment of more than 600 amniote genomes, which is to our knowledge the largest multiple vertebrate genome alignment created so far., The Progressive Cactus program can create reference-free alignments of hundreds of large vertebrate genomes efficiently, and is used for the alignment of more than 600 amniote genomes.

The Zoonomia Project is investigating the genomics of shared and specialized traits in eutherian mammals. Here we provide genome assemblies for 131 species, of which all but 9 are previously uncharacterized, and describe a whole-genome alignment of 240 species of considerable phylogenetic diversity, comprising representatives from more than 80% of mammalian families. We find that regions of reduced genetic diversity are more abundant in species at a high risk of extinction, discern signals of evolutionary selection at high resolution and provide insights from individual reference genomes. By prioritizing phylogenetic diversity and making data available quickly and without restriction, the Zoonomia Project aims to support biological discovery, medical research and the conservation of biodiversity., This project was funded by NIH NHGRI R01HG008742 (E.K.K., B.B., D.P.G., R.S., J.T.-M., J.J., H.J.N., B.P. and J. Armstrong), Swedish Research Council Distinguished Professor Award (K.L.-T., V.D.M., E.M. and J.R.S.M.), Swedish Research Council grant 2018-05973 (K.L.-T.), Knut and Alice Wallenberg Foundation (K.L.-T., V.D.M., E.M. and J.R.S.M.), Uppsala University (K.L.-T., V.D.M., E.M., J.R.S.M., J.J., J. Alfoldi and L.G.), Broad Institute Next10 (L.G.), Gladstone Institutes (K.S.P.), NIH NHGRI 5R01HG002939 (A.F.A.S. and R.H.), NIH NHGRI 5U24HG010136 (A.F.A.S. and R.H.), NIH NHGRI 5R01HG010485 (B.P. and M.D.), NIH NHGRI 2U41HG007234 (B.P., M.D. and J. Armstrong), NIH NIA 5PO1AG047200 (V.N.G.), NIH NIA 1UH2AG064706 (V.N.G.), BFU2017-86471-P MINECO/FEDER, UE (T.M.-B.), Secretaria d’Universitats i Recerca and CERCA Programme del Departament d’Economia i Coneixement de la Generalitat de Catalunya GRC 2017 SGR 880 (T.M.-B.), Howard Hughes International Early Career (T.M.-B.), European Research Council Horizon 2020 no. 864203 (T.M.-B.), Obra Social ‘La Caixa’ (T.M.-B.), BBSRC BBS/E/T/000PR9818, BBS/E/T/ 000PR9783 (W.H. and W.N.), BBSRC Core Strategic Programme Grant BB/P016774/1 (W.H., W.N. and F.D.), Sir Henry Dale Fellowship 200517/Z/16/Z jointly funded by the Wellcome Trust and the Royal Society (N.R.C.), FJCI-2016-29558 MICINN (D.J.), Prince Albert II Foundation of Monaco and Canada, Global Genome Initiative, Smithsonian Institution (M.N.), European Research Council Research Grant ERC-2012-StG311000 (E.C.T.), Irish Research Council Laureate Award (E.C.T.), UK Medical Research Council MR/P026028/1 (W.H. and W.N.), National Science Foundation DEB-1457735 (M.S.S.), National Science Foundation DEB-1753760 (W.J.M.), National Science Foundation IOS-2029774 (E.K.K. and D.P.G.), Robert and Rosabel Osborne Endowment (H.A.L. and J.D.), Swedish Research Council, FORMAS 221-2012-1531 (J.R.S.M.), NSF RoL: FELS: EAGER: DEB 1838283 (D.A.R.) and Academy of Finland grant to Center of Excellence in Tumor Genetics Research no. 312042 (T.K. and J.T.).

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool ( ext-link-type="uri" xlink:href="http://cancerlandscapes.org/">cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets.

We describe a comprehensive map of putative transcription factor binding sites (TFBSs) across multiple genomes created using a search method that relies on hidden Markov models built from experimentally determined TFBSs. Using the information in the TRANSFAC and JASPAR databases, we built 1134 models for TFBSs and used them to scan regions 10 kb upstream of the start of the transcript for all known genes in the human, mouse and Drosophila melanogaster genomes. The results, together with homology information on clusters of ortholog genes across the three genomes, were used to create a multi-organism catalog of annotated TFBSs. The catalog can be queried through a web interface accessible at http://bio.chip.org/mapper that allows the identification, visualization and selection of TFBSs occurring in the promoter of a gene of interest and also the common factors predicted to bind across the cluster of orthologs that includes that gene. Alternatively, the interface allows the user to retrieve binding sites for a single transcription factor of interest in a single gene or in all genes of the human, mouse or fruit fly genomes.

The success rate of association studies can be improved by selecting better genetic markers for genotyping or by providing better leads for identifying pathogenic single nucleotide polymorphisms (SNPs) in the regions of linkage disequilibrium with positive disease associations. We have developed a novel algorithm to predict pathogenic single amino acid changes, either nonsynonymous SNPs (nsSNPs) or missense mutations, in conserved protein domains. Using a Bayesian framework, we found that the probability of a microbial missense mutation causing a significant change in phenotype depended on how much difference it made in several phylogenetic, biochemical, and structural features related to the single amino acid substitution. We tested our model on pathogenic allelic variants (missense mutations or nsSNPs) included in OMIM, and on the other nsSNPs in the same genes (from dbSNP) as the nonpathogenic variants. As a result, our model predicted pathogenic variants with a 10% false-positive rate. The high specificity of our prediction algorithm should make it valuable in genetic association studies aimed at identifying pathogenic SNPs.

Tumor-initiating cells are a subpopulation in aggressive cancers that exhibit traits shared with stem cells, including the ability to self-renew and differentiate, commonly referred to as stemness. In addition, such cells are resistant to chemo- and radiation therapy posing a therapeutic challenge. To uncover stemness-associated functions in glioma-initiating cells (GICs), transcriptome profiles were compared to neural stem cells (NSCs) and gene ontology analysis identified an enrichment of Ca2+ signaling genes in NSCs and the more stem-like (NSC-proximal) GICs. Functional analysis in a set of different GIC lines regarding sensitivity to disturbed homeostasis using A23187 and Thapsigargin, revealed that NSC-proximal GICs were more sensitive, corroborating the transcriptome data. Furthermore, Ca2+ drug sensitivity was reduced in GICs after differentiation, with most potent effect in the NSC-proximal GIC, supporting a stemness-associated Ca2+ sensitivity. NSCs and the NSC-proximal GIC line expressed a larger number of ion channels permeable to potassium, sodium and Ca2+. Conversely, a higher number of and higher expression levels of Ca2+ binding genes that may buffer Ca2+, were expressed in NSC-distal GICs. In particular, expression of the AMPA glutamate receptor subunit GRIA1, was found to associate with Ca2+ sensitive NSC-proximal GICs, and decreased as GICs differentiated along with reduced Ca2+ drug sensitivity. The correlation between high expression of Ca2+ channels (such as GRIA1) and sensitivity to Ca2+ drugs was confirmed in an additional nine novel GIC lines. Calcium drug sensitivity also correlated with expression of the NSC markers nestin (NES) and FABP7 (BLBP, brain lipid-binding protein) in this extended analysis. In summary, NSC-associated NES+/FABP7(+)/GRIA1(+) GICs were selectively sensitive to disturbances in Ca2+ homeostasis, providing a potential target mechanism for eradication of an immature population of malignant cells.

Summary: The serial analysis of chromatin occupancy technique (SACO) promises to become a widely used method for the unbiased genome-wide experimental identification of loci bound by a transcription factor of interest. We describe the first web-based automatic tool, termed sequence tag analysis and reporting tool (START), for processing SACO data generated by experiments performed for the yeast, fruit fly, mouse, rat or human genomes. The program uses as input sequences of inserts from a SACO library from which it extracts all SACO tags, maps them to genomic locations and annotates them. START returns detailed information about these tags including the genes, the genomic elements and the miRNA precursors found in their vicinity, and makes use of the MAPPER database to identify putative transcription factor binding sites located close to the tags. Availability: The program is available at Contact: vdmarinescu@chip.org Supplementary information: Supplementary information is available at

Skeletal muscle differentiation is a complex, highly coordinated process that relies on precise temporal gene expression patterns. To better understand this cascade of transcriptional events, we used expression profiling to analyze gene expression in a 12-day time course of differentiating C2C12 myoblasts. Cluster analysis specific for time-ordered microarray experiments classified 2895 genes and ESTs with variable expression levels between proliferating and differentiating cells into 22 clusters with distinct expression patterns during myogenesis. Expression patterns for several known and novel genes were independently confirmed by real-time quantitative RT-PCR and/or Western blotting and immunofluorescence. MyoD and MEF family members exhibited unique expression kinetics that were highly coordinated with cell-cycle withdrawal regulators. Among genes with peak expression levels during cell cycle withdrawal were Vcam1, Itgb3, Itga5, Vcl, as well as Ptger4, a gene not previously associated with the process of myogenesis. One interesting uncharacterized transcript that is highly induced during myogenesis encodes several immunoglobulin repeats with sequence similarity to titin, a large sarcomeric protein. These data sets identify many additional uncharacterized transcripts that may play important functions in muscle cell proliferation and differentiation and provide a baseline for comparison with C2C12 cells expressing various mutant genes involved in myopathic disorders.

Gene arrays demonstrate a promising ability to characterize expression levels across the entire genome but suffer from significant levels of measurement noise. We present a rigorous new approach to estimate transcript levels and ratios from one or more gene array experiments, given a model of measurement noise and available prior information. The Bayesian estimation of array measurements (BEAM) technique provides a principled method to identify changes in expression level, combine repeated measurements, or deal with negative expression level measurements. BEAM is more flexible than existing techniques, because it does not assume a specific functional form for noise and prior models. Instead, it relies on computational techniques that apply to a broad range of models. We use Affymetrix yeast chip data to illustrate the process of developing accurate noise and prior models from existing experimental data. The resulting noise model includes novel features such as heavy-tailed additive noise and a gene-specific bias term. We also verify that the resulting noise and prior models fit data from an Affymetrix human chip set.

Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity. We describe the implementation of a search method for putative transcription factor binding sites (TFBSs) based on hidden Markov models built from alignments of known sites. We built 1,079 models of TFBSs using experimentally determined sequence alignments of sites provided by the TRANSFAC and JASPAR databases and used them to scan sequences of the human, mouse, fly, worm and yeast genomes. In several cases tested the method identified correctly experimentally characterized sites, with better specificity and sensitivity than other similar computational methods. Moreover, a large-scale comparison using synthetic data showed that in the majority of cases our method performed significantly better than a nucleotide weight matrix-based method. The search engine, available at http://mapper.chip.org , allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, fly, worm and yeast genomes. In addition it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. Due to its extensive database of models, powerful search engine and flexible interface, MAPPER represents an effective resource for the large-scale computational analysis of transcriptional regulation.